What is a Scale Aggregation Block?
A Scale Aggregation Block is a deep learning technique used to concatenate feature maps of images at a wide range of scales. It does so by generating feature maps for each scale using a combination of downsampling, convolution, and upsampling operations. This computational module can easily replace any operator, including convolutional layers.
How Does a Scale Aggregation Block Work?
Assume we have L scales. For each scale l, the following operations are conducted sequentially:
- Downsampling: A downsampling operation (Dl) is applied to the original image, generating a compressed image X'.
- Transformation: A transformation operation (Tl) is applied to the compressed image X', producing a new set of feature maps Y'.
- Upsampling: An upsampling operation (Ul) is applied to the feature maps Y', resulting in upscaled feature maps Y.
After generating the feature maps for each scale, they are concatenated together using the "vertical bar" symbol ( | ). The final output feature maps of the Scale Aggregation Block are produced using this concatenation.
In the reference implementation of this technique, the downsampling operation uses a max pool layer with a specified kernel size and stride, while the upsampling operation is done using resizing and nearest neighbor interpolation.
Why Use a Scale Aggregation Block?
Scale Aggregation Blocks are useful in image processing tasks where multiple scales are needed to capture different patterns in the data. By concatenating the feature maps of different scales, this technique can capture and combine information across several resolutions, leading to improved performance and accuracy of machine learning models. The Scale Aggregation Block is a useful tool in various computer vision applications, including object detection, segmentation, and image classification.
The Scale Aggregation Block is a standard computational module that can be used to improve the performance of machine learning models in various image processing tasks. The technique combines feature maps of different scales by using downsampling, convolution, and upsampling operations. This module replaces any given convolutional layer or series of layers, making it an adaptable and versatile tool for deep learning applications. Scale Aggregation Blocks have demonstrated increased accuracy and improved performance in various computer vision tasks, making them an essential technique for researchers and practitioners in the field.
